recipe r-bmix

Binomial and Beta-Binomial mixture models for counts data.

Homepage:

https://github.com/caravagnalab/BMix

Documentation:

https://caravagnalab.github.io/BMix/

License:

GPL3 / GPL-3.0-or-later

Recipe:

/r-bmix/meta.yaml

BMix provides univariate Binomial and Beta-Binomial mixture models. Count-based mixtures can be used in a variety of settings, for instance to model genome sequencing data of somatic mutations in cancer. BMix fits these mixtures by maximum likelihood exploiting the Expectation Maximization algorithm. Model selection for the number of mixture components is by the Integrated Classification Likelihood, an extension of the Bayesian Information Criterion that includes the entropy of the latent variables.

package r-bmix

versions:

1.0.0-0

depends r-base:

>=4.4,<4.5.0a0

depends r-cli:

depends r-cowplot:

depends r-crayon:

depends r-dplyr:

depends r-easypar:

depends r-ggplot2:

depends r-knitr:

depends r-markdown:

depends r-pio:

depends r-tibble:

depends r-vgam:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install r-bmix

and update with::

   mamba update r-bmix

To create a new environment, run:

mamba create --name myenvname r-bmix

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/r-bmix:<tag>

(see `r-bmix/tags`_ for valid values for ``<tag>``)

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